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Computer Aided Virtual Manufacturing Using Creo Parametric, Kanife Paul Obiora


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Цена: 13060.00р.
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Автор: Kanife Paul Obiora
Название:  Computer Aided Virtual Manufacturing Using Creo Parametric
ISBN: 9783319233581
Издательство: Springer
Классификация:






ISBN-10: 3319233580
Обложка/Формат: Hardback
Страницы: 582
Вес: 1.18 кг.
Дата издания: 28.10.2015
Язык: English
Иллюстрации: 1251 colour illustrations, 970 colour tables, biography
Размер: 167 x 245 x 44
Читательская аудитория: Professional & vocational
Подзаголовок: Easy to learn step by step guide
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Computer Aided Virtual Manufacturing Using Creo Parametric


Creo Parametric 4.0 Black Book (Colored)

Автор: Verma Gaurav, Weber Matt
Название: Creo Parametric 4.0 Black Book (Colored)
ISBN: 1988722039 ISBN-13(EAN): 9781988722030
Издательство: Неизвестно
Цена: 10560.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Creo Parametric 4.0 Black Book

Автор: Verma Gaurav, Weber Matt
Название: Creo Parametric 4.0 Black Book
ISBN: 1988722020 ISBN-13(EAN): 9781988722023
Издательство: Неизвестно
Цена: 8276.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Parametric Building Design Using Autodesk Maya

Автор: Tang Ming
Название: Parametric Building Design Using Autodesk Maya
ISBN: 041564447X ISBN-13(EAN): 9780415644471
Издательство: Taylor&Francis
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Цена: 7348.00 р.
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Описание: Including case studies from Zaha Hadid Architects, Greg Lynn Form, Gage Clemenceau Architects, Tang & Yang Architects, as well as step by step exercises, and demonstration projects; and crucially a fantastic online resource which includes video tutorials, scripts, and Maya source files, this book explores the application of Autodesk Maya in the design process.

Nonlinear Time Series / Nonparametric and Parametric Methods

Автор: Fan Jianqing, Yao Qiwei
Название: Nonlinear Time Series / Nonparametric and Parametric Methods
ISBN: 0387261427 ISBN-13(EAN): 9780387261423
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This book presents the contemporary statistical methods and theory of nonlinear time series analysis. The principal focus is on nonparametric and semiparametric techniques developed in the last decade. It covers the techniques for modelling in state-space, in frequency-domain as well as in time-domain. To reflect the integration of parametric and nonparametric methods in analyzing time series data, the book also presents an up-to-date exposure of some parametric nonlinear models, including ARCH/GARCH models and threshold models. A compact view on linear ARMA models is also provided. Data arising in real applications are used throughout to show how nonparametric approaches may help to reveal local structure in high-dimensional data. Important technical tools are also introduced. The book will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have the value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, population biology and ecology. The prerequisites are basic courses in probability and statistics. Jianqing Fan, coauthor of the highly regarded book Local Polynomial Modeling, is Professor of Statistics at the University of North Carolina at Chapel Hill and the Chinese University of Hong Kong. His published work on nonparametric modeling, nonlinear time series, financial econometrics, analysis of longitudinal data, model selection, wavelets and other aspects of methodological and theoretical statistics has been recognized with the Presidents' Award from the Committee of Presidents of Statistical Societies, the Hettleman Prize for Artistic and Scholarly Achievement from the University of North Carolina, and by his election as a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Qiwei Yao is Professor of Statistics at the London School of Economics and Political Science. He is an elected member of the International Statistical Institute, and has served on the editorial boards for the Journal of the Royal Statistical Society (Series B) and the Australian and New Zealand Journal of Statistics.

Non-Standard Parametric Statistical Inference

Автор: Cheng Russell C H
Название: Non-Standard Parametric Statistical Inference
ISBN: 0198505043 ISBN-13(EAN): 9780198505044
Издательство: Oxford Academ
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Цена: 19404.00 р.
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Описание: This research monograph gives a unified view of non-standard estimation problems. It provides an overall mathematical framework, but also draws together and studies in detail a large number of practical problems, previously only treated separately, offering solution methods and numerical procedures for each.

Combined Parametric-Nonparametric Identification of Block-Oriented Systems

Автор: Grzegorz Mzyk
Название: Combined Parametric-Nonparametric Identification of Block-Oriented Systems
ISBN: 3319035959 ISBN-13(EAN): 9783319035956
Издательство: Springer
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Цена: 14365.00 р.
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Описание: This volume introduces a variety of combined parametric-nonparametric algorithms aimed at resolving problems with block-oriented non-linear dynamic system identification in the presence of random disturbances. It includes analysis of their limit properties.

Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life

Автор: M.S. Nikulin; N. Balakrishnan; Mounir Mesbah; Niko
Название: Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life
ISBN: 146126491X ISBN-13(EAN): 9781461264910
Издательство: Springer
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Цена: 15372.00 р.
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Описание:

Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis and related fields.

Specific topics covered include:

* non-parametric estimation of lifetimes of subjects exposed to radiation

* statistical analysis of simultaneous degradation-mortality data with covariates of the aged

* estimation of maintenance efficiency in semiparametric imperfect repair models

* cancer prognosis using survival forests

* short-term health problems related to air pollution: analysis using semiparametric generalized additive models

* parametric models in accelerated life testing and fuzzy data

* semiparametric models in the studies of aging and longevity

This book will be of use as a reference text for general statisticians, theoreticians, graduate students, reliability engineers, health researchers, and biostatisticians working in applied probability and statistics.

Examples in Parametric Inference with R

Автор: Ulhas Jayram Dixit
Название: Examples in Parametric Inference with R
ISBN: 9811008884 ISBN-13(EAN): 9789811008887
Издательство: Springer
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Цена: 9362.00 р.
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Описание: The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests.

Stable Parametric Programming

Автор: S. Zlobec
Название: Stable Parametric Programming
ISBN: 0792371399 ISBN-13(EAN): 9780792371397
Издательство: Springer
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Цена: 19564.00 р.
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Описание: Optimality and stability are two important notions in applied mathematics. This book is a study of these notions and their relationship in linear and convex parametric programming models. Written in an analytic spirit, it begins with a survey of basic optimality conditions in nonlinear programming.

Advances in Sensitivity Analysis and Parametric Programming

Автор: Tomas Gal; H.J. Greenberg
Название: Advances in Sensitivity Analysis and Parametric Programming
ISBN: 146137796X ISBN-13(EAN): 9781461377962
Издательство: Springer
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Цена: 34937.00 р.
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Описание: The standard view of Operations Research/Management Science (OR/MS) dichotomizes the field into deterministic and probabilistic (nondeterministic, stochastic) subfields.


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